Development of a Rough Based Decision Model for the Diagnostics of Computer Networks

Data on Information Systems is important in any type of enterprise. The data is often used to interpret information and make decisions. In reality, the data that is needed will not always be obtained. Data will be vague and incomplete making it difficult to produce any conclusion. Knowing the right and necessary attributes to obtain is important especially if you have limited time and resources. Coming up with the correct conclusion even with minimal information is a great advantage. This research proposed a Rough Set Based Decision model for the diagnostics of Computer Networks. Applying the model produced in this research will make it possible to produce the correct conclusion even with incomplete information. Knowing the right and necessary attributes to obtain will save time and resources. This paper also proposed a theorem based on the Rough Set Theory about the Information Dependency of Data. Dependent meaning it is the essential Information needed in order to satisfy the Possible Cause. Given certain conditions the Computer Network Technician will only need to verify if a certain Symptom exist in order to discover the Possible Cause. The rules of the Decision Model were verified using the Empirical Testing which resulted in 100% validity. The suggestions outputted by the System were verified by comparing these with previous live data. After the testing process was done, the System obtained a 92% score.